用自监督方法修复水下图像标注质量不稳问题，提升增强效果。
RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision
- 基于扩散模型无训练评估标注质量，量化为噪声等级指导去噪。
- 多阶段去噪机制避免低质标签干扰，提升模型训练稳定性。
- 结合傅里叶网络重建高频细节，适合水下图像增强场景。
水下图像增强对缓解水体介质引起的退化至关重要。尽管基于学习的方法已取得显著进展，但多数依赖标注质量不稳定的配对数据集，制约了模型性能。本文提出一种基于扩散模型的、无需训练的自监督学习策略，用于挖掘训练标签的质量分布。具体而言，通过预训练扩散模型的语义感知嵌入，以无训练方式评估标签质量，并将其量化为噪声等级索引，指导分步去噪过程实现层级监督。该机制在防止低质量标签损害模型的同时，最大化其训练价值。此外，引入基于傅里叶的细化网络，显式重建高频成分。大量实验表明，本方法在恢复质量上持续优于现有最先进方法。代码与预训练模型将在论文接收后公开。
原文摘要 · Abstract (English)
Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise-level indices, guiding a multi-step denoising process for level-wise supervision. This mechanism prevents low-quality labels from degrading the model while maximizing their utility during training. Furthermore, a Fourier-based refinement network is incorporated to explicitly reconstruct high-frequency components. Extensive evaluations demonstrate that our method consistently outperforms SOTA approaches in restoration quality. The code and pre-trained model will be available once accepted in link.
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